Health Condition Impacts in a Nationally Representative Cross-Sectional Survey Vary Substantially by Preference-Based Health Index. (February 2016) - Hanmer, Janel; Cherepanov, Dasha; Palta, Mari; Kaplan, Robert M.; Feeny, David; Fryback, Dennis G.
Importance: Many cost-utility analyses rely on generic utility measures for estimates of disease impact. Commonly used generic preference-based indexes may generate different absolute estimates of disease burden despite sharing anchors of dead at 0 and full health at 1.0.Objective: We compare the impact of 16 prevalent chronic health conditions using 6 utility-based indexes of health and a visual analog scale.Design: Data were from the National Health Measurement Study (NHMS), a cross-sectional telephone survey of 3844 adults aged 35 to 89 years in the United States.Main Outcome Measures: The NHMS included the EuroQol-5D-3L, Health and Activities Limitation Index (HALex), Health Utilities Index Mark 2 (HUI2) and Mark 3 (HUI3), preference-based scoring for the SF-36v2 (SF-6D), Quality of Well-Being Scale, and visual analog scale. Respondents self-reported 16 chronic conditions. Survey-weighted regression analyses for each index with all health conditions, age, and sex were used to estimate health condition impact estimates in terms of quality-adjusted life years (QALYs) lost over 10 years. All analyses were stratified by ages 35 to 69 and 70 to 89 years.Results: There were significant differences between the indexes for estimates of the absolute impact of most conditions. On average, condition impacts were the smallest with the SF-6D and EQ-5D-3L and the largest with the HALex and HUI3. Likewise, the estimated loss of QALYs varied across indexes. Condition impact estimates for EQ-5D-3L, HUI2, HUI3, and SF-6D generally had strong Spearman correlations across conditions (i.e., >0.69).Limitations: This analysis uses cross-sectional data and lacks health condition severity information.Conclusions: Health condition impact estimates vary substantially across the indexes. These results imply that it is difficult to standardize results across cost-utility analyses that use different utility measures.